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Membership inference attacks vs differential privacy

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Evaluating membership inference attacks

These four jupyter notebooks implement classification tasks for Adult, MNIST, and CIFAR-10 (both with a small CNN and with a pretrained model). Membership inference advantage is tested against different regimes of regularization and dropout, and against differential privacy for several values of epsilon.

Requirements:

  • Python 3.8.10
  • TensorFlow 2.5.0
  • TensorFlow-Privacy 0.6.2
  • Optionally CUDA 11.2
  • Scikit-learn
  • Pandas

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Membership inference attacks vs differential privacy

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